A Novel Approach for Selecting Effective Threshold Values in Ternary State Estimation Using Particle Swarm Optimization
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Bibliographic record
Abstract
Inspired by recent breakthroughs in cyber-physical systems (CPSs) and their applications, in this paper, we propose a novel multi-objective method to optimize the threshold values within the ternary event-based framework. To reduce communication overhead, the particle swarm optimization (PSO) approach is applied as an optimizer to identify Pareto optimal set values of the threshold. The proposed optimization technique is subject to constraints to ensure its feasibility. The simulation results confirm the efficiency of the recommended method. Furthermore, the simulation results demonstrate that the proposed framework is comprehensive and capable of finding a wide variety of Pareto optimal ternary event-based state estimations for each predefined threshold.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it